Recently, as a number of distributed applications are used, a persisting information flow is generated from a distributed system. For example, examples of a representative information flow include network traffic data which are used for an intrusion detection system, sensor network data, stock quotations data, and the like. In order to obtain useful information from an information flow, the useful information should be persistingly processed. However, in a conventional database management system (DBMS), since data should be stored and an operation of generating an index should be performed before obtaining information, it is difficult to satisfy these requirements sufficiently. Alternatively, a data stream management system (DSMS) has been proposed, and thus, a system capable of stream processing in a flow of continuously provided data is being widely used.
In terms of characteristic of sensor data, it is difficult to directly apply general raw data to an analysis application, an initial preprocessing operation of processing, refining, and loading raw data should be previously performed.
In conventional load shedding technology, load shedding is performed by ignoring some loads in consideration of network traffic and resources of a server, but since some input data which are helpful to operate a system stably but become an analysis target are ignored, an accuracy of an analysis result is reduced.